Deconditional Downscaling with Gaussian Processes

27 May 2021

Dino Sejdinovic

Papers citing "Deconditional Downscaling with Gaussian Processes"

23 / 23 papers shown

Title
Kernel Quantile Embeddings and Associated Probability Metrics Masha Naslidnyk Siu Lun Chau F. Briol Krikamol Muandet 59 0 0 26 May 2025
Integral Imprecise Probability Metrics Siu Lun Chau Michele Caprio Krikamol Muandet 61 0 0 22 May 2025
Convolutional conditional neural processes for local climate downscaling Anna Vaughan Will Tebbutt J. S. Hosking Richard Turner BDL 43 47 0 20 Jan 2021
Inter-domain Deep Gaussian Processes Tim G. J. Rudner Dino Sejdinovic Yarin Gal 16 11 0 01 Nov 2020
ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows Brian Groenke Luke Madaus C. Monteleoni BDL 22 46 0 11 Aug 2020
Learning from Aggregate Observations Yivan Zhang Nontawat Charoenphakdee Zheng Wu Masashi Sugiyama 30 28 0 14 Apr 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 282 42,038 0 03 Dec 2019
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs Yusuke Tanaka Toshiyuki Tanaka Tomoharu Iwata Takeshi Kurashima Maya Okawa Yasunori Akagi Hiroyuki Toda 25 27 0 19 Jul 2019
Multi-task Learning for Aggregated Data using Gaussian Processes F. Yousefi M. Smith Mauricio A. Alvarez FedML 31 34 0 22 Jun 2019
Multi-resolution Multi-task Gaussian Processes Oliver Hamelijnck Theodoros Damoulas Kangrui Wang Mark Girolami 45 38 0 19 Jun 2019
Kernel Instrumental Variable Regression Rahul Singh M. Sahani Arthur Gretton 74 172 0 01 Jun 2019
Bayesian Deconditional Kernel Mean Embeddings Kelvin Hsu F. Ramos CML BDL 18 9 0 01 Jun 2019
Hyperparameter Learning via Distributional Transfer H. Law P. Zhao Lucian Chan Junzhou Huang Dino Sejdinovic 57 25 0 15 Oct 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Jacob R. Gardner Geoff Pleiss D. Bindel Kilian Q. Weinberger A. Wilson GP 68 1,088 0 28 Sep 2018
Variational Learning on Aggregate Outputs with Gaussian Processes H. Law Dino Sejdinovic E. Cameron T. Lucas Seth Flaxman K. Battle Kenji Fukumizu 39 38 0 22 May 2018
DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution T. Vandal E. Kodra S. Ganguly A. Michaelis R. Nemani A. Ganguly AI4Cl 28 279 0 09 Mar 2017
Uncertain programming model for multi-item solid transportation problem Hasan Dalman 72 732 0 31 May 2016
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 902 149,474 0 22 Dec 2014
Learning Theory for Distribution Regression Z. Szabó Bharath K. Sriperumbudur Barnabás Póczós Arthur Gretton OOD 39 138 0 08 Nov 2014
Kernel Mean Shrinkage Estimators Krikamol Muandet Bharath K. Sriperumbudur Kenji Fukumizu Arthur Gretton Bernhard Schölkopf 48 52 0 21 May 2014
Deep Gaussian Processes Andreas C. Damianou Neil D. Lawrence GP BDL 78 1,178 0 02 Nov 2012
Conditional mean embeddings as regressors - supplementary Steffen Grunewalder Guy Lever Luca Baldassarre Sam Patterson Arthur Gretton Massimiliano Pontil 102 144 0 21 May 2012
Gaussian Process Regression Networks A. Wilson David A. Knowles Zoubin Ghahramani GP BDL 122 192 0 19 Oct 2011